Forward Deployed AI Engineer

• Embed with clients to design and build AI solutions that solve real business problems
• Turn AI capabilities into real-world applications used by customers and stakeholders
• Move AI solutions from experimentation and prototypes into robust, production-ready systems
• Prove AI systems work through evaluation and make them measurably better
• Design and implement RAG architectures, agentic workflows and fine-tuned GenAI applications
• Structure and orchestrate AI-driven applications using GenAI frameworks
• Establish evaluation baselines and metrics for prompts, retrieval, models and parameters
• Build and manage experiment tracking, model versioning, observability and deployment workflows
• Develop automated monitoring, evaluation and CI/CD pipelines for ML and LLM systems
• Design scalable training and inference pipelines
• Advise clients, translate business challenges into technical roadmaps and lead projects from scoping through delivery
• Explain architecture decisions to CTOs and pair-program with engineers to debug pipelines
• Work hands-on with Databricks, PySpark, SQL, Delta Lake, Unity Catalog, MLflow and Mosaic AI features

• Experience building AI systems across the full lifecycle, from experimentation to deployment and monitoring
• Experience building production-grade GenAI applications, including RAG architectures, agentic workflows and fine-tuning
• Experience with platforms such as OpenAI, Anthropic, Azure AI or similar
• Experience with GenAI frameworks such as LangChain, LangGraph, Pydantic AI, Hugging Face or DSPy
• Evaluation-driven approach: establish baselines, define meaningful metrics and measure improvements
• Understanding of LLMOps and MLOps, including experiment tracking, model versioning, observability and deployment
• Experience building automated monitoring, evaluation and CI/CD pipelines for traditional ML and LLMs
• Experience designing scalable training and inference data pipelines for production
• Extensive Python experience with data/ML libraries and ability to write clean, testable, well-structured code
• Experience deploying or working with AI solutions in AWS, Azure or GCP
• Client-facing consultant mindset: translate business challenges into technical roadmaps and lead projects from scoping to delivery
• Databricks experience or Databricks certification is nice to have
• Experience with PyTorch or TensorFlow is nice to have
• Experience with production LLMOps tooling, including evaluation, monitoring and drift detection, is nice to have

• Employment facilitated through a third-party Employer of Record/payroll provider in line with Hungarian employment requirements
• Access to Databricks learning resources, certifications and partner academies
• Regular internal knowledge-sharing sessions and workshops
• Opportunities to work hands-on with modern data and AI technologies, including Databricks and the wider data engineering ecosystem
• Flexible and collaborative working environment, with remote and hybrid working options
• International team collaboration with experienced data and AI professionals across Europe
• Growth opportunities to develop technical expertise, consulting skills and technical leadership
• Regular team activities and opportunities to connect with colleagues across the wider RevoData team
• Sustainable ways of working and wellbeing-focused environment
• Seven-month crash course covering certifications, hands-on client project work and internal knowledge-sharing sessions for candidates without Databricks experience, transitioning to a permanent contract upon agreed milestones